Results 41 to 50 of about 20,583 (298)
JOIV: International Journal on Informatics VisualizationThe classification of skin cancer is crucial as the chance of survival increases significantly with timely and accurate treatment. Convolution Neural Networks (CNNs) have proven effective in classifying skin cancer. However, CNN models are often regarded
Sin Yi Hong, Lih Poh Lin
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Survey of XAI in Digital Pathology [PDF]
, 2020 Artificial intelligence (AI) has shown great promise for diagnostic imaging assessments. However, the application of AI to support medical diagnostics in clinical routine comes with many challenges. The algorithms should have high prediction accuracy but also be transparent, understandable and reliable.
Milda Poceviciute +2 more
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Advanced Robotics Research, EarlyView.This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao +6 more
wiley +1 more source
Научно-технический вестник информационных технологий, механики и оптикиThe issues of trust in decisions made (formed) by intelligent systems are becoming more and more relevant. A systematic review of Explicable Artificial Intelligence (XAI) methods and tools aimed at bridging the gap between the complexity of neural ...
D. N. Biryukov, A. S. Dudkin
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Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging
Diagnostics, 2023 Advances in artificial intelligence (AI), especially deep learning (DL), have facilitated magnetic resonance imaging (MRI) data analysis, enabling AI-assisted medical image diagnoses and prognoses.
Jinzhao Qian +3 more
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Designer-User Communication for XAI: An epistemological approach to discuss XAI design
CoRR, 2021 Artificial Intelligence is becoming part of any technology we use nowadays. If the AI informs people's decisions, the explanation about AI's outcomes, results, and behavior becomes a necessary capability. However, the discussion of XAI features with various stakeholders is not a trivial task.
Juliana Jansen Ferreira +1 more
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Advanced Science, EarlyView.We propose the Full‐Body AI Agent, a multi‐scale collaborative framework with 7 biological‐layer agents. It unifies multi‐omics/clinical data via standardized protocols, enabling phenotype‐guided closed‐loop reasoning, quantitative evaluation, and LLM safeguards, with promising applications in tumor metastasis modeling and precision drug development ...
Aoqi Wang +11 more
wiley +1 more source
Interpretable Ensemble-Based Intrusion Detection Using Feature Selection on the ToN_IoT Dataset
JISA (Jurnal Informatika dan Sains)With With the rapid growth of IoT, securing interconnected devices against cyber threats has become critical. IoT datasets such as ToN-IoT are often high-dimensional, which poses challenges for efficient and accurate intrusion detection.
Vaman Shakir Sulaiman +1 more
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Applications of Explainable Artificial Intelligence in Diagnosis and Surgery
Diagnostics, 2022 In recent years, artificial intelligence (AI) has shown great promise in medicine. However, explainability issues make AI applications in clinical usages difficult.
Yiming Zhang, Ying Weng, Jonathan Lund
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Trustworthy XAI and Application
CoRRArtificial Intelligence (AI) is an important part of our everyday lives. We use it in self-driving cars and smartphone assistants. People often call it a "black box" because its complex systems, especially deep neural networks, are hard to understand. This complexity raises concerns about accountability, bias, and fairness, even though AI can be quite ...
Nasim, MD Abdullah Al +6 more
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